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PREDICTION FOR HEAVY RAINFALL USING NEURAL NETWORKS

使用神经网络预测大雨

基本信息

批准号：
04805049
负责人：
MORIYAMA Toshiyuki
金额：
$ 1.28万
依托单位：
KYUSHU UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

项目摘要

The goal herein is to establish a prediction method for a degree of risk by certain rainfalls , as a countermeasure against sediment disaster which caused by rain. Thus, the time of concentration and critical rainfall are defined, A prediction system using neural network is constructed. It is expected that the neural network can learn a general rule from examples.The main procedure of the method are as follows ; cumulative rainfalls on various times are calculated from a rainfall time series. The rainfalls pattern which is normalized and teach signal of occurrence 0.99 or non-occurrence 0.01 of disaster are given to the system. After the neural network is optimized by back-propagation learning, the system automatically calculates the degree of risk from 0 to 1 for new rainfalls pattern.The prediction system is applied to three types of real cases and the results are as follows ; It is applicable for predicting the occurrence of debris flows at Unzen volcano, because the system has high accuracy in judgment. On secondary disaster after typhoon No.9119 at the Chikugo river basin, change in critical rainfall after the typhoon is recognized. Moreover the time of concentration and critical rainfall are found. It is clarified that the antecedent rainfall affects the occurrence of sediment disasters in Kagoshima city.This system is good tool for prediction of sediment disaster and to estimate the occurrence criteria. Therefore, the prediction system using the neural network for the prediction of the rainfall rate seems to be developing more in the future.

本文的目的是建立一种预测方法，以确定某些灾害的风险程度，作为预防降雨泥沙灾害的对策。据此，定义了集中时间和临界降雨量，建立了一个神经网络预报系统。该方法的主要步骤是从一个降雨时间序列中计算出各时刻的累积平均数。系统给出了归一化后的故障模式和发生0.99或未发生0.01的示教信号。通过BP学习对神经网络进行优化后，系统自动计算新的泥石流模式的0 ~ 1的危险度，并将该预测系统应用于三种类型的真实的案例，结果表明：该系统具有较高的判断准确率，适用于云前火山泥石流发生的预测。在9119号台风后筑后江流域的次生灾害中，识别了台风后临界降雨量的变化。通过对鹿儿岛市泥沙灾害的预测，明确了前期降雨对泥沙灾害发生的影响，为泥沙灾害的预测和发生判据的确定提供了良好的工具。因此，使用神经网络预测降雨量的预测系统似乎是未来发展的更多。